Difference between revisions of "Occlusions"
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* [[Conditional Adversarial Architecture (CAA)]] AI Sees Through Walls | * [[Conditional Adversarial Architecture (CAA)]] AI Sees Through Walls | ||
| + | * [[Other Challenges]] in Artificial Intelligence | ||
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Occlusion is a fundamental problem in human pose estimation and many other vision tasks. Instead of hallucinating missing body parts based on visible ones, we demonstrate a solution that leverages radio signals to accurately track the 2D human pose through walls and obstructions. [http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhao_Through-Wall_Human_Pose_CVPR_2018_paper.pdf Through-Wall Human Pose Estimation Using Radio Signals | Mingmin Zhao, Tianhong Li, Mohammad Abu, Alsheikh Yonglong, Tian Hang Zhao, Antonio Torralba, Dina Katabi - MIT CSAIL] | Occlusion is a fundamental problem in human pose estimation and many other vision tasks. Instead of hallucinating missing body parts based on visible ones, we demonstrate a solution that leverages radio signals to accurately track the 2D human pose through walls and obstructions. [http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhao_Through-Wall_Human_Pose_CVPR_2018_paper.pdf Through-Wall Human Pose Estimation Using Radio Signals | Mingmin Zhao, Tianhong Li, Mohammad Abu, Alsheikh Yonglong, Tian Hang Zhao, Antonio Torralba, Dina Katabi - MIT CSAIL] | ||
Revision as of 23:00, 16 February 2019
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- Conditional Adversarial Architecture (CAA) AI Sees Through Walls
- Other Challenges in Artificial Intelligence
Occlusion is a fundamental problem in human pose estimation and many other vision tasks. Instead of hallucinating missing body parts based on visible ones, we demonstrate a solution that leverages radio signals to accurately track the 2D human pose through walls and obstructions. Through-Wall Human Pose Estimation Using Radio Signals | Mingmin Zhao, Tianhong Li, Mohammad Abu, Alsheikh Yonglong, Tian Hang Zhao, Antonio Torralba, Dina Katabi - MIT CSAIL
